{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multivariate Logistic Regression Demo\n",
    "\n",
    "_Source: 🤖[Homemade Machine Learning](https://github.com/trekhleb/homemade-machine-learning) repository_\n",
    "\n",
    "> ☝Before moving on with this demo you might want to take a look at:\n",
    "> - 📗[Math behind the Logistic Regression](https://github.com/trekhleb/homemade-machine-learning/tree/master/homemade/logistic_regression)\n",
    "> - ⚙️[Logistic Regression Source Code](https://github.com/trekhleb/homemade-machine-learning/blob/master/homemade/logistic_regression/logistic_regression.py)\n",
    "\n",
    "**Logistic regression** is the appropriate regression analysis to conduct when the dependent variable is dichotomous (binary). Like all regression analyses, the logistic regression is a predictive analysis. Logistic regression is used to describe data and to explain the relationship between one dependent binary variable and one or more nominal, ordinal, interval or ratio-level independent variables.\n",
    "\n",
    "Logistic Regression is used when the dependent variable (target) is categorical.\n",
    "\n",
    "For example:\n",
    "\n",
    "- To predict whether an email is spam (`1`) or (`0`).\n",
    "- Whether online transaction is fraudulent (`1`) or not (`0`).\n",
    "- Whether the tumor is malignant (`1`) or not (`0`).\n",
    "\n",
    "> **Demo Project:** In this example we will train clothes classifier that will recognize clothes types (10 categories) from `28x28` pixel images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To make debugging of logistic_regression module easier we enable imported modules autoreloading feature.\n",
    "# By doing this you may change the code of logistic_regression library and all these changes will be available here.\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "# Add project root folder to module loading paths.\n",
    "import sys\n",
    "sys.path.append('../..')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Dependencies\n",
    "\n",
    "- [pandas](https://pandas.pydata.org/) - library that we will use for loading and displaying the data in a table\n",
    "- [numpy](http://www.numpy.org/) - library that we will use for linear algebra operations\n",
    "- [matplotlib](https://matplotlib.org/) - library that we will use for plotting the data\n",
    "- [math](https://docs.python.org/3/library/math.html) - math library that we will use to calculate sqaure roots etc.\n",
    "- [logistic_regression](https://github.com/trekhleb/homemade-machine-learning/blob/master/homemade/logistic_regression/logistic_regression.py) - custom implementation of logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import 3rd party dependencies.\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "import math\n",
    "\n",
    "# Import custom logistic regression implementation.\n",
    "from homemade.logistic_regression import LogisticRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the Data\n",
    "\n",
    "In this demo we will use a sample of [Fashion MNIST dataset in a CSV format](https://www.kaggle.com/zalando-research/fashionmnist).\n",
    "\n",
    "Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set. Each example is a 28x28 grayscale image, associated with a label from 10 classes. Zalando intends Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits.\n",
    "\n",
    "Instead of using full dataset with 60000 training examples we will use cut dataset of just 5000 examples that we will also split into training and testing sets.\n",
    "\n",
    "Each row in the dataset consists of 785 values: the first value is the label (a category from 0 to 9) and the remaining 784 values (28x28 pixels image) are the pixel values (a number from 0 to 255).\n",
    "\n",
    "Each training and test example is assigned to one of the following labels:\n",
    "\n",
    "- 0 T-shirt/top\n",
    "- 1 Trouser\n",
    "- 2 Pullover\n",
    "- 3 Dress\n",
    "- 4 Coat\n",
    "- 5 Sandal\n",
    "- 6 Shirt\n",
    "- 7 Sneaker\n",
    "- 8 Bag\n",
    "- 9 Ankle boot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "   label  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  pixel8  \\\n",
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       "\n",
       "   pixel780  pixel781  pixel782  pixel783  pixel784  \n",
       "0         0         0         0         0         0  \n",
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       "\n",
       "[10 rows x 785 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the data.\n",
    "data = pd.read_csv('../../data/fashion-mnist-demo.csv')\n",
    "\n",
    "# Laets create the mapping between numeric category and category name.\n",
    "label_map = {\n",
    "    0: 'T-shirt/top',\n",
    "    1: 'Trouser',\n",
    "    2: 'Pullover',\n",
    "    3: 'Dress',\n",
    "    4: 'Coat',\n",
    "    5: 'Sandal',\n",
    "    6: 'Shirt',\n",
    "    7: 'Sneaker',\n",
    "    8: 'Bag',\n",
    "    9: 'Ankle boot',\n",
    "}\n",
    "\n",
    "# Print the data table.\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the Data\n",
    "\n",
    "Let's peek first 25 rows of the dataset and display them as an images to have an example of clothes we will be working with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# How many images to display.\n",
    "numbers_to_display = 25\n",
    "\n",
    "# Calculate the number of cells that will hold all the images.\n",
    "num_cells = math.ceil(math.sqrt(numbers_to_display))\n",
    "\n",
    "# Make the plot a little bit bigger than default one.\n",
    "plt.figure(figsize=(10, 10))\n",
    "\n",
    "# Go through the first images in a training set and plot them.\n",
    "for plot_index in range(numbers_to_display):\n",
    "    # Extrace image data.\n",
    "    digit = data[plot_index:plot_index + 1].values\n",
    "    digit_label = digit[0][0]\n",
    "    digit_pixels = digit[0][1:]\n",
    "\n",
    "    # Calculate image size (remember that each picture has square proportions).\n",
    "    image_size = int(math.sqrt(digit_pixels.shape[0]))\n",
    "    \n",
    "    # Convert image vector into the matrix of pixels.\n",
    "    frame = digit_pixels.reshape((image_size, image_size))\n",
    "    \n",
    "    # Plot the image matrix.\n",
    "    plt.subplot(num_cells, num_cells, plot_index + 1)\n",
    "    plt.imshow(frame, cmap='Greys')\n",
    "    plt.title(label_map[digit_label])\n",
    "    plt.tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
    "\n",
    "# Plot all subplots.\n",
    "plt.subplots_adjust(hspace=0.5, wspace=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split the Data Into Training and Test Sets\n",
    "\n",
    "In this step we will split our dataset into _training_ and _testing_ subsets (in proportion 80/20%).\n",
    "\n",
    "Training data set will be used for training of our model. Testing dataset will be used for validating of the model. All data from testing dataset will be new to model and we may check how accurate are model predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data set on training and test sets with proportions 80/20.\n",
    "# Function sample() returns a random sample of items.\n",
    "pd_train_data = data.sample(frac=0.8)\n",
    "pd_test_data = data.drop(pd_train_data.index)\n",
    "\n",
    "# Convert training and testing data from Pandas to NumPy format.\n",
    "train_data = pd_train_data.values\n",
    "test_data = pd_test_data.values\n",
    "\n",
    "# Extract training/test labels and features.\n",
    "num_training_examples = 3000\n",
    "x_train = train_data[:num_training_examples, 1:]\n",
    "y_train = train_data[:num_training_examples, [0]]\n",
    "\n",
    "x_test = test_data[:, 1:]\n",
    "y_test = test_data[:, [0]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Init and Train Logistic Regression Model\n",
    "\n",
    "> ☝🏻This is the place where you might want to play with model configuration.\n",
    "\n",
    "- `polynomial_degree` - this parameter will allow you to add additional polynomial features of certain degree. More features - more curved the line will be.\n",
    "- `max_iterations` - this is the maximum number of iterations that gradient descent algorithm will use to find the minimum of a cost function. Low numbers may prevent gradient descent from reaching the minimum. High numbers will make the algorithm work longer without improving its accuracy.\n",
    "- `regularization_param` - parameter that will fight overfitting. The higher the parameter, the simplier is the model will be.\n",
    "- `polynomial_degree` - the degree of additional polynomial features (`x1^2 * x2, x1^2 * x2^2, ...`). This will allow you to curve the predictions.\n",
    "- `sinusoid_degree` - the degree of sinusoid parameter multipliers of additional features (`sin(x), sin(2*x), ...`). This will allow you to curve the predictions by adding sinusoidal component to the prediction curve.\n",
    "- `normalize_data` - boolean flag that indicates whether data normalization is needed or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up linear regression parameters.\n",
    "max_iterations = 10000  # Max number of gradient descent iterations.\n",
    "regularization_param = 25  # Helps to fight model overfitting.\n",
    "polynomial_degree = 0  # The degree of additional polynomial features.\n",
    "sinusoid_degree = 0  # The degree of sinusoid parameter multipliers of additional features.\n",
    "normalize_data = True  # Whether we need to normalize data to make it more unifrom or not. \n",
    "\n",
    "# Init logistic regression instance.\n",
    "logistic_regression = LogisticRegression(x_train, y_train, polynomial_degree, sinusoid_degree, normalize_data)\n",
    "\n",
    "# Train logistic regression.\n",
    "(thetas, costs) = logistic_regression.train(regularization_param, max_iterations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Print Training Results\n",
    "\n",
    "Let's see how model parameters (thetas) look like. For each digit class (from 0 to 9) we've just trained a set of 784 parameters (one theta for each image pixel). These parameters represents the importance of every pixel for specific digit recognition. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>-0.048227</td>\n",
       "      <td>0.062439</td>\n",
       "      <td>0.022960</td>\n",
       "      <td>0.149320</td>\n",
       "      <td>...</td>\n",
       "      <td>0.153883</td>\n",
       "      <td>0.079365</td>\n",
       "      <td>-0.033011</td>\n",
       "      <td>-0.019675</td>\n",
       "      <td>0.017837</td>\n",
       "      <td>-0.025347</td>\n",
       "      <td>-0.026005</td>\n",
       "      <td>-0.003478</td>\n",
       "      <td>-0.002177</td>\n",
       "      <td>-0.009066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-5.334694</td>\n",
       "      <td>-0.003845</td>\n",
       "      <td>-0.037747</td>\n",
       "      <td>-0.110560</td>\n",
       "      <td>-0.115551</td>\n",
       "      <td>-0.028090</td>\n",
       "      <td>-0.106846</td>\n",
       "      <td>-0.041750</td>\n",
       "      <td>-0.220657</td>\n",
       "      <td>-0.049796</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002940</td>\n",
       "      <td>0.159790</td>\n",
       "      <td>0.163778</td>\n",
       "      <td>0.086334</td>\n",
       "      <td>0.027404</td>\n",
       "      <td>-0.098383</td>\n",
       "      <td>-0.014745</td>\n",
       "      <td>-0.087777</td>\n",
       "      <td>0.167204</td>\n",
       "      <td>0.146124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-6.609060</td>\n",
       "      <td>-0.010289</td>\n",
       "      <td>-0.009757</td>\n",
       "      <td>-0.029398</td>\n",
       "      <td>-0.002841</td>\n",
       "      <td>-0.013853</td>\n",
       "      <td>0.065444</td>\n",
       "      <td>0.152862</td>\n",
       "      <td>0.039919</td>\n",
       "      <td>-0.109553</td>\n",
       "      <td>...</td>\n",
       "      <td>0.070574</td>\n",
       "      <td>-0.061689</td>\n",
       "      <td>0.028969</td>\n",
       "      <td>-0.204451</td>\n",
       "      <td>-0.200882</td>\n",
       "      <td>-0.112483</td>\n",
       "      <td>-0.078020</td>\n",
       "      <td>-0.024253</td>\n",
       "      <td>-0.008831</td>\n",
       "      <td>-0.001139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-5.965323</td>\n",
       "      <td>0.000036</td>\n",
       "      <td>-0.007320</td>\n",
       "      <td>0.010527</td>\n",
       "      <td>0.044382</td>\n",
       "      <td>-0.019542</td>\n",
       "      <td>-0.009529</td>\n",
       "      <td>-0.023622</td>\n",
       "      <td>0.027513</td>\n",
       "      <td>-0.055903</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013763</td>\n",
       "      <td>0.171588</td>\n",
       "      <td>-0.147751</td>\n",
       "      <td>-0.045985</td>\n",
       "      <td>0.086505</td>\n",
       "      <td>0.058849</td>\n",
       "      <td>0.084423</td>\n",
       "      <td>-0.070254</td>\n",
       "      <td>-0.055217</td>\n",
       "      <td>-0.002108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-11.772900</td>\n",
       "      <td>-0.000465</td>\n",
       "      <td>-0.001262</td>\n",
       "      <td>-0.037732</td>\n",
       "      <td>-0.001665</td>\n",
       "      <td>-0.004291</td>\n",
       "      <td>-0.001232</td>\n",
       "      <td>-0.010755</td>\n",
       "      <td>-0.021838</td>\n",
       "      <td>-0.059554</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.061065</td>\n",
       "      <td>-0.070279</td>\n",
       "      <td>-0.038202</td>\n",
       "      <td>-0.073953</td>\n",
       "      <td>-0.083174</td>\n",
       "      <td>-0.073595</td>\n",
       "      <td>-0.013555</td>\n",
       "      <td>-0.012098</td>\n",
       "      <td>-0.091695</td>\n",
       "      <td>-0.005742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-4.922826</td>\n",
       "      <td>0.142705</td>\n",
       "      <td>-0.033088</td>\n",
       "      <td>-0.002464</td>\n",
       "      <td>-0.113538</td>\n",
       "      <td>-0.093484</td>\n",
       "      <td>-0.035009</td>\n",
       "      <td>-0.045016</td>\n",
       "      <td>0.047347</td>\n",
       "      <td>0.026366</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.096220</td>\n",
       "      <td>0.025678</td>\n",
       "      <td>-0.108178</td>\n",
       "      <td>-0.043293</td>\n",
       "      <td>-0.179027</td>\n",
       "      <td>-0.050755</td>\n",
       "      <td>0.083261</td>\n",
       "      <td>0.115971</td>\n",
       "      <td>-0.025531</td>\n",
       "      <td>-0.170215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-9.278711</td>\n",
       "      <td>-0.000367</td>\n",
       "      <td>-0.000285</td>\n",
       "      <td>-0.000301</td>\n",
       "      <td>-0.000410</td>\n",
       "      <td>-0.000456</td>\n",
       "      <td>-0.000550</td>\n",
       "      <td>-0.001178</td>\n",
       "      <td>-0.003780</td>\n",
       "      <td>-0.007860</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.050218</td>\n",
       "      <td>-0.051179</td>\n",
       "      <td>-0.013316</td>\n",
       "      <td>-0.013148</td>\n",
       "      <td>-0.005308</td>\n",
       "      <td>-0.005978</td>\n",
       "      <td>-0.005408</td>\n",
       "      <td>-0.006201</td>\n",
       "      <td>-0.003152</td>\n",
       "      <td>-0.000013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-5.977974</td>\n",
       "      <td>-0.000269</td>\n",
       "      <td>-0.040986</td>\n",
       "      <td>-0.061915</td>\n",
       "      <td>-0.019404</td>\n",
       "      <td>-0.065937</td>\n",
       "      <td>0.122967</td>\n",
       "      <td>0.033192</td>\n",
       "      <td>0.107072</td>\n",
       "      <td>0.096812</td>\n",
       "      <td>...</td>\n",
       "      <td>0.065897</td>\n",
       "      <td>0.037445</td>\n",
       "      <td>-0.030971</td>\n",
       "      <td>0.014127</td>\n",
       "      <td>-0.048779</td>\n",
       "      <td>-0.025388</td>\n",
       "      <td>-0.033395</td>\n",
       "      <td>-0.028944</td>\n",
       "      <td>-0.036300</td>\n",
       "      <td>0.002200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-7.507192</td>\n",
       "      <td>-0.000174</td>\n",
       "      <td>-0.000325</td>\n",
       "      <td>-0.001354</td>\n",
       "      <td>-0.000743</td>\n",
       "      <td>-0.001619</td>\n",
       "      <td>-0.001899</td>\n",
       "      <td>-0.005294</td>\n",
       "      <td>-0.011630</td>\n",
       "      <td>-0.013474</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.033911</td>\n",
       "      <td>-0.029371</td>\n",
       "      <td>-0.015877</td>\n",
       "      <td>-0.003029</td>\n",
       "      <td>-0.004274</td>\n",
       "      <td>0.012986</td>\n",
       "      <td>-0.001100</td>\n",
       "      <td>0.016288</td>\n",
       "      <td>0.117543</td>\n",
       "      <td>-0.000238</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 785 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         0         1         2         3         4         5         6    \\\n",
       "0  -6.188828 -0.098811 -0.087328 -0.015862  0.024045  0.121492  0.058710   \n",
       "1  -6.474232 -0.002895 -0.002513 -0.009509 -0.011743  0.117668 -0.048227   \n",
       "2  -5.334694 -0.003845 -0.037747 -0.110560 -0.115551 -0.028090 -0.106846   \n",
       "3  -6.609060 -0.010289 -0.009757 -0.029398 -0.002841 -0.013853  0.065444   \n",
       "4  -5.965323  0.000036 -0.007320  0.010527  0.044382 -0.019542 -0.009529   \n",
       "5 -11.772900 -0.000465 -0.001262 -0.037732 -0.001665 -0.004291 -0.001232   \n",
       "6  -4.922826  0.142705 -0.033088 -0.002464 -0.113538 -0.093484 -0.035009   \n",
       "7  -9.278711 -0.000367 -0.000285 -0.000301 -0.000410 -0.000456 -0.000550   \n",
       "8  -5.977974 -0.000269 -0.040986 -0.061915 -0.019404 -0.065937  0.122967   \n",
       "9  -7.507192 -0.000174 -0.000325 -0.001354 -0.000743 -0.001619 -0.001899   \n",
       "\n",
       "        7         8         9      ...          775       776       777  \\\n",
       "0 -0.042991  0.026648 -0.019745    ...    -0.051891 -0.039698  0.264672   \n",
       "1  0.062439  0.022960  0.149320    ...     0.153883  0.079365 -0.033011   \n",
       "2 -0.041750 -0.220657 -0.049796    ...    -0.002940  0.159790  0.163778   \n",
       "3  0.152862  0.039919 -0.109553    ...     0.070574 -0.061689  0.028969   \n",
       "4 -0.023622  0.027513 -0.055903    ...     0.013763  0.171588 -0.147751   \n",
       "5 -0.010755 -0.021838 -0.059554    ...    -0.061065 -0.070279 -0.038202   \n",
       "6 -0.045016  0.047347  0.026366    ...    -0.096220  0.025678 -0.108178   \n",
       "7 -0.001178 -0.003780 -0.007860    ...    -0.050218 -0.051179 -0.013316   \n",
       "8  0.033192  0.107072  0.096812    ...     0.065897  0.037445 -0.030971   \n",
       "9 -0.005294 -0.011630 -0.013474    ...    -0.033911 -0.029371 -0.015877   \n",
       "\n",
       "        778       779       780       781       782       783       784  \n",
       "0  0.098661  0.124377  0.036166 -0.053816 -0.046484 -0.026023 -0.013350  \n",
       "1 -0.019675  0.017837 -0.025347 -0.026005 -0.003478 -0.002177 -0.009066  \n",
       "2  0.086334  0.027404 -0.098383 -0.014745 -0.087777  0.167204  0.146124  \n",
       "3 -0.204451 -0.200882 -0.112483 -0.078020 -0.024253 -0.008831 -0.001139  \n",
       "4 -0.045985  0.086505  0.058849  0.084423 -0.070254 -0.055217 -0.002108  \n",
       "5 -0.073953 -0.083174 -0.073595 -0.013555 -0.012098 -0.091695 -0.005742  \n",
       "6 -0.043293 -0.179027 -0.050755  0.083261  0.115971 -0.025531 -0.170215  \n",
       "7 -0.013148 -0.005308 -0.005978 -0.005408 -0.006201 -0.003152 -0.000013  \n",
       "8  0.014127 -0.048779 -0.025388 -0.033395 -0.028944 -0.036300  0.002200  \n",
       "9 -0.003029 -0.004274  0.012986 -0.001100  0.016288  0.117543 -0.000238  \n",
       "\n",
       "[10 rows x 785 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print thetas table.\n",
    "pd.DataFrame(thetas)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Illustrate Hidden Layers Perceptrons\n",
    "\n",
    "Each perceptron in the hidden layer learned something from the training process. What it learned is represented by input theta parameters for it. Each perceptron in the hidden layer has 28x28 input thetas (one for each input image pizel). Each theta represents how valuable each pixel is for this particuar perceptron. So let's try to plot how valuable each pixel of input image is for each perceptron based on its theta values.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# How many images to display.\n",
    "numbers_to_display = 9\n",
    "\n",
    "# Calculate the number of cells that will hold all the images.\n",
    "num_cells = math.ceil(math.sqrt(numbers_to_display))\n",
    "\n",
    "# Make the plot a little bit bigger than default one.\n",
    "plt.figure(figsize=(10, 10))\n",
    "\n",
    "# Go through the thetas and print them.\n",
    "for plot_index in range(numbers_to_display):\n",
    "    # Extrace thetas data.\n",
    "    digit_pixels = thetas[plot_index][1:]\n",
    "\n",
    "    # Calculate image size (remember that each picture has square proportions).\n",
    "    image_size = int(math.sqrt(digit_pixels.shape[0]))\n",
    "    \n",
    "    # Convert image vector into the matrix of pixels.\n",
    "    frame = digit_pixels.reshape((image_size, image_size))\n",
    "    \n",
    "    # Plot the thetas matrix.\n",
    "    plt.subplot(num_cells, num_cells, plot_index + 1)\n",
    "    plt.imshow(frame, cmap='Greys')\n",
    "    plt.title(plot_index)\n",
    "    plt.tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
    "\n",
    "# Plot all subplots.\n",
    "plt.subplots_adjust(hspace=0.5, wspace=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze Gradient Descent Progress\n",
    "\n",
    "The plot below illustrates how the cost function value changes over each iteration. You should see it decreasing. \n",
    "\n",
    "In case if cost function value increases it may mean that gradient descent missed the cost function minimum and with each step it goes further away from it.\n",
    "\n",
    "From this plot you may also get an understanding of how many iterations you need to get an optimal value of the cost function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw gradient descent progress for each label.\n",
    "labels = logistic_regression.unique_labels\n",
    "for index, label in enumerate(labels):\n",
    "    plt.plot(range(len(costs[index])), costs[index], label=label_map[labels[index]])\n",
    "\n",
    "plt.xlabel('Gradient Steps')\n",
    "plt.ylabel('Cost')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate Model Training Precision\n",
    "\n",
    "Calculate how many of training and test examples have been classified correctly. Normally we need test precission to be as high as possible. In case if training precision is high and test precission is low it may mean that our model is overfitted (it works really well with the training data set but it is not good at classifying new unknown data from the test dataset). In this case you may want to play with `regularization_param` parameter to fighth the overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Precision: 93.8000%\n",
      "Test Precision: 83.8000%\n"
     ]
    }
   ],
   "source": [
    "# Make training set predictions.\n",
    "y_train_predictions = logistic_regression.predict(x_train)\n",
    "y_test_predictions = logistic_regression.predict(x_test)\n",
    "\n",
    "# Check what percentage of them are actually correct.\n",
    "train_precision = np.sum(y_train_predictions == y_train) / y_train.shape[0] * 100\n",
    "test_precision = np.sum(y_test_predictions == y_test) / y_test.shape[0] * 100\n",
    "\n",
    "print('Training Precision: {:5.4f}%'.format(train_precision))\n",
    "print('Test Precision: {:5.4f}%'.format(test_precision))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Test Dataset Predictions\n",
    "\n",
    "In order to illustrate how our model classifies unknown examples let's plot first 64 predictions for testing dataset. All green clothes on the plot below have been recognized corrctly but all the red clothes have not been recognized correctly by our classifier. On top of each image you may see the clothes class (type) that has been recognized on the image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 64 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# How many numbers to display.\n",
    "numbers_to_display = 64\n",
    "\n",
    "# Calculate the number of cells that will hold all the numbers.\n",
    "num_cells = math.ceil(math.sqrt(numbers_to_display))\n",
    "\n",
    "# Make the plot a little bit bigger than default one.\n",
    "plt.figure(figsize=(15, 15))\n",
    "\n",
    "# Go through the first numbers in a test set and plot them.\n",
    "for plot_index in range(numbers_to_display):\n",
    "    # Extrace digit data.\n",
    "    digit_label = y_test[plot_index, 0]\n",
    "    digit_pixels = x_test[plot_index, :]\n",
    "    \n",
    "    # Predicted label.\n",
    "    predicted_label = y_test_predictions[plot_index][0]\n",
    "\n",
    "    # Calculate image size (remember that each picture has square proportions).\n",
    "    image_size = int(math.sqrt(digit_pixels.shape[0]))\n",
    "    \n",
    "    # Convert image vector into the matrix of pixels.\n",
    "    frame = digit_pixels.reshape((image_size, image_size))\n",
    "    \n",
    "    # Plot the number matrix.\n",
    "    color_map = 'Greens' if predicted_label == digit_label else 'Reds'\n",
    "    plt.subplot(num_cells, num_cells, plot_index + 1)\n",
    "    plt.imshow(frame, cmap=color_map)\n",
    "    plt.title(label_map[predicted_label])\n",
    "    plt.tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
    "\n",
    "# Plot all subplots.\n",
    "plt.subplots_adjust(hspace=0.5, wspace=0.5)\n",
    "plt.show()"
   ]
  }
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